Senior Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Apr, 26

Salary

234700.0

Posted On

02 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Predictive Analytics, Statistics, Data Science, Programming, SQL, Big Data, Distributed Computing, Anomaly Detection, Time-Series Analysis, LLMs, Prompt Engineering, Data-Driven Approaches, Model Governance, Responsible AI, Feature Adoption, Agentic Orchestration

Industry

Software Development

Description

Overview We are building large-scale, Azure-based intelligence platform that transforms complex data into high-quality and rich actionable insights to Microsoft MSN Feeds. The system integrates advanced machine learning models with emerging agentic capabilities powered by large language models (LLMs) to model recommendations, automate analysis, generate contextual summaries, and streamline workflows across organizational tools. As a Senior Applied Scientist, you will lead the development of these Machine Learning solutions leveraging SOTA technologies in GenAI to build predictive models for generating recommendations, detecting anomalies, generating automated insights with reasoning and ensuring the platform delivers accurate, actionable intelligence at scale. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction. Responsibilities • Lead the design and implementation of machine learning models for recommendations, anomaly detection, and actionable insights. • Drive experimentation and validation of models. Mentor junior scientists and contribute to model governance and Responsible AI practices. • Partner with engineering and BI teams to operationalize insights into dashboards and alerting systems. • Advance feature adoption scoring and health check analytics through data-driven approaches. • Engineer optimal prompts for various LLM calls, using chain-of-thought and other advanced techniques. • Generate account planning guidance, tailored talking points, pitch assets, follow ups using agentic orchestration. Qualifications Required Qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Preferred Qualifications: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 3+ years of hands-on experience developing machine learning or statistical models to solve real-world problems (in industry or academic projects), including building and validating algorithms such as regressions, classifiers, or clustering models. Proficiency in programming for data science (e.g. using Python or R for data analysis and modeling) and experience with data querying languages (e.g. SQL). Big Data & Distributed Computing: Hands-on experience with large-scale data processing using tools like Apache Spark or Azure Databricks for training and inference workflows. Advanced Analytics: Skilled in time-series analysis and anomaly detection techniques (e.g., ARIMA, isolation forests) applied to business contexts for actionable insights. LLMs & Domain Adaptation: Practical experience with prompt engineering, fine-tuning GPT-like models, and applying LLMs in domain-heavy areas (healthcare, agriculture, social sciences) while ensuring privacy and Responsible AI compliance. #MicrosoftAI Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

Lead the design and implementation of machine learning models for recommendations and actionable insights. Mentor junior scientists and partner with engineering teams to operationalize insights.